openinterp-mcp 0.2.0

MCP server + Colab backend for mechanistic interpretability research. Bring-your-own-agent. Privacy-first.

openinterp-mcp

v0.1.0 beta · API may shift before v1.0

MCP server + Colab backend for mechanistic interpretability research. Bring your own agent. Works with Claude Code, Cursor, Cline, OpenHands, Aider, or any harness that speaks MCP. Privacy-first. We do not host inference. We do not custody your keys. Your Colab session, your model, your data.

What this is

A research toolkit that turns probe-causality and SAE-feature experiments into agent-callable primitives. Researchers run the model on their own compute (Colab Pro recommended), expose an HTTPS endpoint over ngrok, and let an LLM agent drive experiments via 8 typed MCP tools.

The 8 MCP primitives:

Tool	What it does
colab_attach	Attach to a running Colab/vast.ai/runpod session via its public HTTPS URL. Validates /health, caches the endpoint locally.
colab_status	Health check — loaded model, probes in memory, captures held.
list_probes	List probes currently loaded in the backend (model_id, layer, position, source).
capture_acts	Run a forward pass with hooks, extract activations at specified layers/positions. Returns capture_id.
probe_eval	Apply a loaded probe to a stored capture, return AUROC + per-sample scores.
steer	Inject direction×α at layer L. Returns base + steered generation + control-token-normalized Δrel (paper-6 protocol).
sae_lookup	Decompose a stored activation into top-K SAE features with auto-interp descriptions.
causality_protocol	Run the three mandatory checks (random-feature baseline, control-token norm, structural-rigidity α-sweep) and emit a verdict in {causal, weak-causal, epiphenomenal-softmax, epiphenomenal-template, undetermined}.

Publish + judge primitives are Python modules, not MCP tools. Use from openinterp_mcp.publish import publish to submit to the Atlas (HF Dataset + Zenodo DOI + registry PR), and from openinterp_mcp.judge import reproduce for Claude-Code-as-judge replication. These run outside the MCP request/response loop because they take minutes (long-running side effects).

Architecture (privacy-first)

USER'S MACHINE (laptop)              USER'S COMPUTE (Colab/vast.ai/runpod)
├── Claude Code / Cursor / Cline     ├── Colab Secrets (HF/OAI/Anthropic keys)
├── openinterp-mcp (stateless)       ├── FastAPI + 8 endpoints
└── ~/.openinterp/sessions.json      ├── Qwen3.6-27B + probes loaded
    (URLs cached, no secrets)        └── ngrok / cloudflared tunnel
                                          │
              ←── HTTPS (ngrok URL) ──────┘

DOES NOT EXIST ANYWHERE:
✗ api.openinterp.org inference endpoint
✗ a server custodying your keys
✗ telemetry / logs traversing our infra
✗ a database of your queries

Quick start (researchers)

1. In a Colab notebook (one cell)

%pip install openinterp-mcp[colab] -q
from google.colab import userdata
import os
for k in ['HF_TOKEN', 'OPENAI_API_KEY', 'ANTHROPIC_API_KEY', 'NGROK_AUTHTOKEN']:
    try: os.environ[k] = userdata.get(k)
    except: pass

from openinterp_mcp.colab import launch
url = launch(model="Qwen/Qwen2.5-7B-Instruct")
print(f"\n✓ OpenInterp session ready.\n  Paste in Claude:  /colab-attach {url}\n")

2. In Claude Code / Cursor / Cline

/colab-attach https://abc123.ngrok-free.app
✓ Connected. Qwen2.5-7B loaded. 5 probes available.

/capture-acts "Solve x^2 = 4" --layers L11,L20,L27 --positions end_question
/probe-eval saturation-direction-L20 --acts last_capture
/causality-protocol L20_pre_tool

Install (agent-side)

pip install openinterp-mcp

Add to claude_desktop_config.json (or equivalent for Cursor/Cline):

{
  "mcpServers": {
    "openinterp": {
      "command": "openinterp-mcp",
      "args": ["serve"]
    }
  }
}

Status

v0.0.1 alpha — Phase 1 of an 11-phase build documented at openinterp.org/mcp. Track progress at github.com/OpenInterpretability/openinterp-mcp/issues.

License

Apache-2.0. See LICENSE.